Why Every Hospital and Health System Should Be Adopting AI Agents

This summer marked three years since my brother, Aman, and I founded Adonis. We started the company with a clear but complex goal: to solve the huge issues US healthcare providers face when securing payment from insurance companies. Three years later, we have helped tens of thousands of providers manage their payments through our platform. Across physician groups, hospitals, and digital health companies, we have developed an intimate understanding of the various challenges these providers contend with. It’s been one of the most fulfilling journeys of my career. Now, I’m excited to share the knowledge Adonis has cultivated through an ongoing newsletter series.  The first installment focuses on our biggest takeaway: why every health system should adopt AI agents.

AI agents are advanced, autonomous software that combine intelligent automation with proactive problem-solving. Unlike traditional software, they don’t just wait for instructions. They’re built to take in real-time data, weigh it against a set of goals, and actually make decisions on their own. Sometimes that looks like a straightforward, reactive system. Other times, it’s a far more advanced agent—one that learns as it goes, adapts, and gets better over time. These range from voice agents that field patient phone calls to browser agents that navigate web pages, analyze databases, and take notes. At their best, these systems operate with a kind of independence that starts to feel less like a tool and more like a partner.

I believe agentic AI can help streamline nearly every aspect of any health system. From verifying a patient’s benefits more quickly, to increasing the accuracy of diagnostics and treatment, to ensuring a healthcare provider is reimbursed by payers without unnecessary delay, AI agents can help with all of this.

Improving Clinical Processes and Patient Care
A repeated issue I hear time and again from the providers that Adonis works with is the pressure that health systems face: doctors and medical staff are overworked, healthcare costs are only increasing, and patients often bear the brunt of these challenges.

This is where the help of AI agents is not only a reprieve, but soon enough, I think will be considered essential for providing quality care. For our providers who work with AI agents, they are already helping with the hiring of hospital staff—from posting jobs, to screening resumes, and scheduling interviews. On the patient’s side, AI agents take care of time-consuming and error-prone processes like the verification of benefits and prior authorization. Instead of waiting on staff to make phone calls or search through payer portals, AI agents can verify the eligibility of my benefits (including any coverage gaps) in real time. This speeds up the verification process, and prevents a claim denial in the future. And most importantly, as a patient, I’m fully aware of my financial responsibility before I walk into the doctor’s office. 

AI agents can also manage the schedules of medical staff and patients, assist doctors with complex clinical decisions through predictive analytics, and help with diagnostics and patient monitoring. I especially love what an AI agent can offer a patient in this regard—if you’re in the hospital, an agent can monitor your vital signs around the clock, and proactively sense warning signals to alert doctors before they become critical. And if you’re at home, AI agents can monitor you around the clock through wearable devices, ultimately offering more comprehensive patient care and lowering readmission rates.

Across the clinical spectrum, processes that used to take hours each day now take moments, and effectively, there are more hands on deck. I have repeatedly seen the relief and increased bandwidth it offers providers, and the improved care it offers patients. 

Improving Claim Reimbursement And Claim Denials
While the benefits AI agents provide patients and medical staff are clear, I have seen how transformational AI agents are to the revenue cycle management (RCM) of health systems, too.  

Due to payers creating constantly-shifting payment policies, receiving payment has never been more difficult for healthcare providers. Claim denial rates are ever-increasing, and payers often use AI to detect issues and deny claims at scale, leading to automatic rejections that cause a never-ending cycle of costly and time-consuming work for providers. At Adonis, alleviating this has been our daily mission for the last three years.

In my view, the key is to play payers at their own game. By deploying AI across the revenue cycle, providers can cut days in accounts receivable and sharply reduce claim denials.

When it comes to claim reimbursements, the system has been broken for as long as I can remember. Delays pile up because communication with payers is clunky at best, follow-up drags on forever, and humans—no matter how good they are—can’t flawlessly track the thousands of claims that flow through every month. The result? Frustration, wasted time, and cash flow held hostage.

This is exactly where AI agents are starting to flip the script. They handle the work that never stops, running 24/7 and scaling effortlessly, so human teams don’t have to. They’re in constant dialogue with payers, making sure issues are caught and resolved as early as possible, so payment actually comes in on time. And because there’s no manual data entry involved, the error rate drops to almost nothing. One of our healthcare partners saw their time-to-payment shrink by 35% just by handing claim follow-up to an AI agent—an amazing result.

But delays are only half the battle—denials are another monster altogether. Breaking them down is usually complex, tedious work—time-consuming in a way that drains teams. AI agents, though, can now review, scrub, and adjust claims before they ever get submitted, dramatically reducing denials in the first place. And when denials do come back, AI can analyze them at scale—spotting the patterns, surfacing the root causes, and equipping RCM teams to fix systemic issues for the long term. It’s not just about putting out fires—it’s about redesigning the system so fewer fires break out in the first place. One of our partners, ApolloMD, saw a 90%+ success rate in autonomous issue resolution thanks to AI agents.

AI agents don’t just make things faster—they make them scalable. Instead of RCM teams drowning in manual labor, they get to step back and actually lead—shaping strategy, driving revenue forward, and focusing on the kinds of challenges where human judgment, creativity, and empathy really matter. 

Looking ahead
The more I see the results that AI agents offer the health systems we work with, the more I see AI agents as a no-brainer to transforming the way we tackle the critical issues facing health systems across the country and the wider world.

With AI agents, I believe that any health system will not only be able to meet some of the biggest clinical- and revenue-based challenges head-on, but they can proactively build intelligent workflows that adapt to the chaotic, ever-changing medical landscape. If you want to future-proof your healthcare system, befriending these virtual assistants just makes sense. .

Thanks for reading. Until next time!

P.S. For my readers in the New York City area, don’t miss our RCM Innovation Summit later this week. We’ll be exploring how leading hospitals and health systems are deploying AI agents and automation to transform the revenue cycle. Hear directly from experts at Johns Hopkins Medicine, Yale New Haven Health, and Ohio State Wexner Medical Center, and connect with peers across the industry. Attendance is free and we still have some spots open — register here to save your seat!